import torch
from torch.optim import Optimizer
import apex
import unittest

from test_fused_optimizer import TestFusedOptimizer
from itertools import product


class Novograd(Optimizer):
    """
    Implements Novograd algorithm.

    Args:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float, optional): learning rate (default: 1e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square (default: (0.95, 0))
        eps (float, optional): term added to the denominator to improve
            numerical stability (default: 1e-8)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        grad_averaging: gradient averaging
        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`_
            (default: False)
    """

    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.95, 0),
        eps=1e-8,
        weight_decay=0,
        grad_averaging=False,
        amsgrad=False,
    ):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
        defaults = dict(
            lr=lr,
            betas=betas,
            eps=eps,
            weight_decay=weight_decay,
            grad_averaging=grad_averaging,
            amsgrad=amsgrad,
        )

        super(Novograd, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(Novograd, self).__setstate__(state)
        for group in self.param_groups:
            group.setdefault("amsgrad", False)

    def step(self, closure=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
            and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group["params"]:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError("Sparse gradients are not supported.")
                amsgrad = group["amsgrad"]

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state["step"] = 0
                    # Exponential moving average of gradient values
                    state["exp_avg"] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state["exp_avg_sq"] = torch.zeros([]).to(state["exp_avg"].device)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state["max_exp_avg_sq"] = torch.zeros([]).to(state["exp_avg"].device)

                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
                if amsgrad:
                    max_exp_avg_sq = state["max_exp_avg_sq"]
                beta1, beta2 = group["betas"]

                state["step"] += 1

                norm = torch.sum(torch.pow(grad, 2))

                if exp_avg_sq == 0:
                    exp_avg_sq.copy_(norm)
                else:
                    exp_avg_sq.mul_(beta2).add_(norm, alpha=1 - beta2)

                if amsgrad:
                    # Maintains the maximum of all 2nd moment running avg. till now
                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
                    # Use the max. for normalizing running avg. of gradient
                    denom = max_exp_avg_sq.sqrt().add_(group["eps"])
                else:
                    denom = exp_avg_sq.sqrt().add_(group["eps"])

                grad.div_(denom)
                if group["weight_decay"] != 0:
                    grad.add_(p.data, alpha=group["weight_decay"])
                if group["grad_averaging"]:
                    grad.mul_(1 - beta1)
                exp_avg.mul_(beta1).add_(grad)

                p.data.add_(exp_avg, alpha=-group["lr"])

        return loss


class TestFusedNovoGrad(TestFusedOptimizer):
    def __init__(self, *args, **kwargs):
        super(TestFusedNovoGrad, self).__init__(*args, **kwargs)

        # The options for NovoGrad and FusedNovoGrad are very specific if they
        # are expected to behave the same.
        self.options = {
            "lr": 1e-3,
            "betas": (0.95, 0),
            "eps": 1e-8,
            "weight_decay": 0,
            "grad_averaging": False,
            "amsgrad": False,
        }

        self.tst_options = {
            "lr": 1e-3,
            "betas": (0.95, 0),
            "eps": 1e-8,
            "weight_decay": 0,
            "grad_averaging": False,
            "amsgrad": False,
            "bias_correction": False,
            "reg_inside_moment": True,
            "norm_type": 2,
            "init_zero": False,
            "set_grad_none": True,
        }

        self.ref_optim = Novograd
        self.fused_optim = apex.optimizers.FusedNovoGrad

    def test_float(self):
        self.gen_single_type_test(param_type=torch.float)

    def test_half(self):
        self.gen_single_type_test(param_type=torch.float16)

    @unittest.skipIf(torch.cuda.device_count() < 2, "more than 1 GPU required")
    def test_multi_device(self):
        devices = ("cuda:1", "cuda:0")
        for current_dev, tensor_dev in product(devices, devices):
            with torch.cuda.device(current_dev):
                torch.cuda.synchronize()
                self.gen_single_type_test(param_type=torch.float, device=tensor_dev)

    def test_multi_params(self):
        sizes = [[4096, 1024], [4096], [4096, 2048], [32320, 1024], [1]]

        tensors = []
        for size in sizes:
            tensors.append(torch.rand(size, dtype=torch.float, device="cuda"))
        ref_param, tst_param, ref_optim, tst_optim = self.gen_param_optim(
            tensors, self.options, self.tst_options
        )

        for _ in range(self.iters):
            self.gen_grad(ref_param, tst_param)
            ref_optim.step()
            tst_optim.step()
            max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
            self.assertLessEqual(max_abs_diff, self.max_abs_diff)
            self.assertLessEqual(max_rel_diff, self.max_rel_diff)


if __name__ == "__main__":
    unittest.main()
